# CONFIGURE
# change the path to your project
path = 'F:/tf_projects/CNN/yolo3'
data_path='F:/ProjectData/yolo3/newdata'
MODEL_PATH=path+"/save_model/TEETH_DETECT"

# image pré-processing
Input_shape = 416  # width=height # 608 or 416 or 320
channels = 3  # RBG
MAX_BOX_NUM=18
# training
# score = 0.3
# iou = 0.7

# batch_size = 32
score_threshold = 0.2  # 保留>  初始0.3  识别置信 越大越苛刻 最优推测0.2  不影响训练
# 原始代码中，两个作用 1 训练时滤掉相对于label模糊的预测不优化； 2. 推测时滤掉接近最优预的其他预测
# 预测时要是太大，会出现很多框，导致padding 报错
ignore_thresh = 0.3  # 保留<  训练0.5  重合少的算无目标 越小越苛刻 最优推测0.3  影响训练
'''
0.3     0.05
0.5     0.3
'''



model_type='N'
scale_num={'N':3,'T':2}[model_type]

type_dict={1:0,2:0,3:0,14:0,15:0,16:0,30:0,31:0,32:0,17:0,18:0,19:0,
           4:1,5:1,12:1,13:1,28:1,29:1,20:1,21:1,
           6:2,11:2,27:2,22:2,
           7:3,8:3,9:3,10:3,23:3,24:3,25:3,26:3,
           }

num_class=4

